System and method for adaptive text recommendation

ABSTRACT

Network system provides a real-time adaptive recommendation set of documents with a high statistical measure of relevancy to the requestor device. The recommendation set is optimized based on analyzing text of documents of the interest set, categorizing these documents into clusters, extracting keywords representing the themes or concepts of documents in the clusters, and filtering a population of eligible documents accessible to the system utilizing site and or Internet-wide search engines. The system is either automatically or manually invoked and it develops and presents the recommendation set in real-time. The recommendation set may be presented as a greeting, notification, alert, HTML fragment, fax, voicemail, or automatic classification or routing of customer e-mail, personal e-mail, job postings, and offers for sale or exchange.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation and claims the prioritybenefit of U.S. patent application Ser. No. 11/927,450 filed Oct. 29,2007, which is a continuation and claims the priority benefit of U.S.patent application Ser. No. 11/003,920 filed Dec. 3, 2004, now U.S. Pat.No. 8,645,389, which is a continuation and claims the priority benefitof U.S. patent application Ser. No. 09/723,855 filed Nov. 27, 2000, nowU.S. Pat. No. 6,845,374, the disclosures of which are incorporatedherein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Invention relates to a method and system for recommending relevant itemsto a user of an electronic network. More particularly, the presentinvention relates to a means of analyzing the text of documents ofinterest and recommending a set of documents with a high measure ofstatistical relevancy.

2. Description of the Related Art

Most personalization and web user analysis (also known as “clickstream”)technologies work with the system making a record of select web pagesthat a user has viewed, typically in a web log. A web log entry recordswhich users looked at which web pages in the site. A typical web logentry consist of two major pieces of information, namely, first, someform of user identifier such as an IP address, a cookie ID, or a sessionID, and second, some form of page identifier such as a URL, file name,or product number. Additional information may be included such as thepage the user came from to get to the page and the time when the userrequested the page. The web log entry records are collected in a filesystem of a web server and analyzed using software to produce charts ofpage requests per day or most visited pages, etc. Such softwaretypically relies on simple aggregations and summarizations of pagerequests rather than any analysis of the internal page structure andcontent.

Other personalization software also relies on the concept of web logs.The dominant technology is collaborative filtering, which works byobserving the pages of the web site a user requests, searching for otherusers that have made similar requests, and suggesting pages that theseother users requested. For example, if a user requests pages 1 and 2, acollaborative filtering system would find others who did the same. Ifthe other users on the average also requested pages 3 and 4, acollaborative system would offer pages 3 and 4 as a best recommendation.Other collaborative filtering systems use statistical techniques toperform frequency analysis and more sophisticated prediction techniquesusing methods such as neural networks. Examples of collaborativefiltering systems include NETPERCEPTIONS™, LIKE MINDS™, and WISEWIRE™.Such a system in action can be viewed at AMAZON.COM™.

Other types of collaborative filtering systems allow users to rank theirinterest in a group of documents. User answers are collected to developa user profile that is compared to other user profiles. The documentviewed by others with the same profile is recommended to the user. Thisapproach may use artificial intelligence techniques such as incrementallearning methods to improve the recommendations based on user feedback.Systems using this approach include SITEHELPER™, SYSKILL & EBERT™, FAB™,LIBRA™, and WEBWATCHER™. However collaborative filtering is ineffectiveto personalize documents with dynamic or unstructured content. Forexample, each auction in an auction web site or item offered in a swapweb site is different and may have no logged history of previous usersto which collaborative filtering can be applied. Collaborative filteringis also not effective for infrequently viewed documents or offerings ofinterest to only a few site visitors.

Clearly, there is a need for a system that considers not only theidentifiers of the pages the user viewed but also the words in the pagesviewed in order to make more focused recommendations to the user.Broadening the concept of pages to documents in general, there is a needfor a recommendation system that analyzes the words in the document auser has expressed interest in. Such a recommendation system shouldsupport options of residing in the same computer as the web site, or ona remote server, or on an end user's computer. Furthermore, the systemshould be able to access documents from external sources such as fromother web sites throughout the Internet or from private networks. Aflexible recommendation system should also support a scalablearchitecture of using a proprietary text search engine or leverage offthe search engines of other web sites or generalized Internet-widesearch engines.

SUMMARY OF THE PRESENTLY CLAIMED INVENTION

Invention discloses methods and systems for adaptively selectingrelevant documents to present to a requester. A requestor device, eithera client working on a PC, or a software program running on a server,automatically or manually invokes the adaptive text recommendationsystem (ATRS) and based on extracted keywords from the text of relateddocuments, a set of relevant documents is presented to the requester.The set of recommended documents is continually updated as moredocuments are added to the set of related documents or interest set.ATRS adapts the choice of recommended documents based on new analysis oftext contained in the interest set, categorizing the documents intoclusters, extracting the keywords that capture the theme or concept ofthe documents in each cluster, and filtering the entire set of eligibledocuments in the application web site and or other web sites to compilethe set of recommended documents with a high measure of statisticalrelevancy.

One embodiment is an application of ATRS in an e-commerce site, such asa seller of goods or services or an auction web site. A client loggingonto an e-commerce site is greeted with a recommended set of relevantgoods, services, or auction items by analyzing the text of the documentsrepresenting items previously bought, ordered, or bid on. As the clientselects an item from the recommended set or an item on the web page,ATRS updates the documents in the interest set, categorizes thedocuments in the interest set into clusters, extracts keywords from theclusters, and filters the eligible set of documents at the web site toconstruct a recommended set. This recommended set of documents isrebuilt possibly every time the client makes a new selection or moves toa different web page.

The recommended set of documents may be presented as a panel or HTMLfragment in a web page being viewed. The recommendations may be orderedfor example by the statistical measure of relevancy or by popularity ofthe item and filtered based on information about the client.

In an alternate embodiment, ATRS may be invoked automatically by asoftware program to develop a recommended set for existing clients notcurrently logged on. The recommendations may take the form of anotification of select clients for sales, special events, or promotions.In other alternate embodiments, the recommendations may take the form ofa client alert or “push” technology data feed. Similarly, otherapplications of ATRS include notification of clients of upcomingtelevision shows, entertainment, or job postings based on the analysisof the text of documents associated with these shows, entertainment orjob openings in which the client has indicated previous interest.

Additional applications of ATRS include automatic classification ofpersonal e-mail, and automatic routing of customer relations e-mail torepresentatives who previously successfully resolved similar types ofe-mail. The recommended set may also consist of Internet bookmarks orsubscriptions to publications for a “community of interest” group.Furthermore, the recommended set may be transmitted as a fax, convertedto audio, video, or an alert on a pager or PDA and transmitted to therequester.

The present invention can be applied to data in general, wherein arequester device issues a request for recommended data comprisingdocuments, audio files, video files or multimedia files and an adaptivedata recommendation system would return a recommended set of such data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B are an architectural diagram and flow diagram, respectively,illustrating an adaptive text recommendation system invoked by arequester device, in one embodiment of the present invention.

FIG. 2 is an architectural diagram of the main components or modules ofan adaptive text recommendation system in one embodiment of the presentinvention.

FIG. 3 is a flow diagram of the main components or modules of anadaptive text recommendation system in one embodiment of the presentinvention.

FIG. 4 is a flow diagram of the assembly processing of ATRS in oneembodiment of the present invention.

FIG. 5 is an architectural diagram of the pre-processing of the interestset of ATRS in one embodiment of the present invention.

FIG. 6 is a flow diagram of the preprocessing of ATRS in one embodimentof the present invention.

FIG. 7 is an architectural diagram of the clustering process of ATRS inone embodiment of the present invention.

FIG. 8 is a flow diagram of the keyword extraction process of ATRS inone embodiment of the present invention.

FIG. 9 is a flow diagram of the recommendation processing of ATRS in oneembodiment of the present invention.

FIG. 10A is an architectural diagram of ATRS operable in the applicationwebsite whereas FIG. 10B is an architectural diagram of ATRS operable ina distributed manner with segments running at the application websiteand at a remote site, according to one embodiment of the presentinvention.

FIG. 11 is an architectural diagram illustrating the deployment ofmultiple applications of ATRS in and outside the United States,according to one embodiment of the present invention.

FIG. 12 is an architectural diagram of an adaptive data recommendationsystem in an alternative embodiment of the present invention,illustrating the data requester device invoking and receiving a set ofrecommended relevant data.

FIG. 13 is an architectural diagram illustrating the major input andoutput of an adaptive data recommendation system in an alternativeembodiment of the present invention, illustrating the various types ofdata that are requested and returned to the requester device.

DETAILED DESCRIPTION

FIG. 1A shows how the requestor device 2 invokes either manually orautomatically a request for a set of relevant documents to ATRS 4 whichprocesses the request and obtains a set of relevant documents from adocument source 6 and returns the set to requester device 2. FIG. 1B isa high level flow diagram of ATRS consisting of steps where ATRS isinvoked manually or automatically by a requester for a set of relevantdocuments 105 and ATRS returns a set of relevant documents 107. Arequester may be a client or a software program. A requester device maybe a client personal computer.

FIG. 2 shows the major modules of one embodiment of the presentinvention. The major modules are: Assembly Module 10, Pre-processingModule 30, Clustering Module 40, Keyword Extraction Module 50,Filtration Module 60, Recommendation Module 80, and Presentation Module90.

The Assembly Module 10 assembles documents from multiple sources into aninterest set. Documents in the interest set may include documents in adatabase considered of interest to the requester, web site pagespreviously viewed by the requestor in the application web site or otherweb sites, documents selected by the requestor from a list obtained by asearch in the application web site or by an Internet-wide search, e-mailsent by the requester, documents transmitted from a remote source suchas those maintained in remote servers or in other private networkdatabases, and documents sent by fax, scanned or input into any type ofcomputer and made available to the Assembly Module 10. For example, inan auction site, the client, presented with a list of live auctionitems, clicks on several auction items that are of interest, theninvokes ATRS to show a set of recommended auction items.

The Pre-processing Module 30 isolates the words in the interest set andremoves words that are not useful for distinguishing one document fromanother document. Words removed are common words in the language andnon-significant words to a specific application of ATRS.

The Clustering Module 40 groups the documents whose words have a highdegree of similarity into clusters.

The Keyword Extraction Module 50 determines the keyword score for eachword in a cluster and selects as keywords for the cluster words with thehighest keyword score and that also appear in a minimum number ofdocuments specified for the application.

The Filtration Module 60 uses application parameters for assemblingdocuments considered eligible for recommendation. Eligible documents mayinclude documents from enterprise databases, documents from privatenetwork databases, documents from the application web site, anddocuments from public networks, such as the Internet. Furthermore, thesedocuments may cover subjects in many fields including but not limited tofinance, law, medicine, business, environment, education, science, andventure capital. Application parameters may include age of documents andor client data that specify inclusion or exclusion of certain documents.

The Recommendation Module 80 calculates the relevance score for eligibledocuments to a cluster and ranks the eligible documents by relevancescore and other application criteria. Top scoring documents are furtherfiltered by criteria specific to the client.

The Presentation Module 90 personalizes the presentation format of therecommendations for the client. Examples of formats are e-mail,greetings to a site visitor, HTML fragment or a list of Internet sites.Any special sorting or additional filtration for the client is applied.The recommendations are converted to the desired medium, such asvoicemail, fax hardcopy, file transfer transmission, or audio/videoalert.

FIG. 3 is a flow chart of one embodiment of the present inventionstarting with the assembly of documents from multiple sources into aninterest set 110; pre-processing of the documents to remove “stop” words112; grouping the documents in the interest set into clusters 114;extraction of keywords contained in documents included in the clusters116; filtration of documents eligible to be considered forrecommendation for each cluster 118; construction of a recommendationset of documents per cluster 120; and presentation of therecommendations 122.

FIG. 4 is a flow chart of the Assembly Module 10 illustrating theprocess involved in assembling all documents which comprise the interestset. Documents previously recorded for the client 130 may includeprevious purchases in a e-commerce site, bids in an auction site, or webpages visited by client which contain tags that automatically triggercommunication to a server of the page or data involved. Documents mayinclude those corresponding to the navigation path of the client in thewebsite 132. The client may have selected documents from a list of webpages 134 as a result of a site search or an Internet-wide search. Otherdocuments may include e-mails, faxed document, scanned documents or anyother form of document input associated with the client 136.Alternatively, documents included may be those transmitted through anetwork for the client 138 where the storage of documents is doneremotely. All input documents are assembled into an interest set 140.

FIG. 5 is an architectural chart illustrating the use of the assembledinterest set 26 and the Stop Word Database 32 in the Pre-processingModule 30 to create the refined interest set of documents 34. The StopWord Database 32 comprises words that are not useful for distinguishingone document from another document in the interest set. If theapplication language is English, examples would include words such as‘and’, ‘the’, and ‘etc.’ The Stop Word Database 32 also includes wordsthat are common in the interest set as a result of the purpose,application or business conducted for the site. For example, on anauction site, each web page containing an item description might alsocontain the notice “Pay with your Visa card!” In this case, the words‘pay’, ‘visa’, and ‘card’ would be included in the Stop Word Database32.

FIG. 6 is a flow chart illustrating the process performed in thePre-processing Module 30 in one embodiment. The process includesisolating words in the documents of the interest set and converting thewords into a common format 150, such as converting the words to lowercase. A word is an alphanumeric string surrounded by white space orpunctuation marks. Next, if a word is a common word of the language 152the word is removed 158. If a word is a non-significant word specific tothe site and the application 154, it is also removed 158. Otherwise, theword is retained in the document 156. In one embodiment, the commonwords of the language and the non-significant words specific to theapplication are maintained in the Stop Word Database 32.

FIG. 7 is an architectural chart illustrating the use of the refinedinterest set 34 and processing in the Clustering Module 40 to group thedocuments into clusters 42, 44, and 46. Clustering is the process ofgrouping together documents in the interest set whose words have a highdegree of similarity. In one embodiment of the present invention, thesimilarity of two documents D₁ and D₂ is denoted by similarity (D₁, D₂).If D₁ does not contain any words in common with D₂, then:

similarity(D ₁ , D ₂)=0.

If the two documents have words in common, then:

${{similarity}( {D_{1},D_{2}} )} = \frac{{{count}( {w,D_{1}} )}{{count}( {w,D_{2}} )}}{\begin{matrix}\lbrack {{{count}( {w,D_{1}} )}{{count}( {w,D_{2}} )}^{2}} \rbrack^{1/2} \\\lbrack {{{count}( {w,D_{1}} )}{{count}( {w,D_{2}} )}^{2}} \rbrack^{1/2}\end{matrix}}$

where count (w, D) denotes the number of occurrences of the word w inthe document D, and w

D₁∩D₂ denotes a word that appears in both D₁ and D₂. Many otherdefinitions of similarity between two documents are possible.

The clustering criteria may vary depending on the application of ATRS 4.An advantageous implementation involves arranging the documents from theinterest set so as to maximize the cluster score, wherein the clusterscore of a cluster containing only one document is zero and the clusterscore for a cluster containing more than one to document is the averagesimilarity score between the documents in the cluster.

The clustering algorithm can be any one of well-known clusteringalgorithms that can be applied to maximize the clustering criterion,such as K-Means, Single-Pass, or Buckshot, which are incorporated byreference.

FIG. 8 is a flow diagram of the keyword extraction processing of ATRS 4in one embodiment of the present invention. For each word w in a clusterC, calculate the frequency of the word w in the interest set,Frequency(w); and calculate the frequency of the word w in cluster C,Frequency(w, C) 180. Calculate the keyword score for word w in thecluster C 182, using the equation:

Keyword score(w,C)=log Frequency(w,C)−log Frequency(w)

Select keywords for cluster C based on application criteria 184; forexample, select keywords that have high scores and appear in severaldocuments. Upon processing all clusters 186, the system proceeds to thebalance of processing. In an alternative embodiment of the presentinvention, the keywords describing the theme or concept in a cluster donot necessarily appear in the text of any document, but insteadsummarize the theme or concept determined, for example, by a method fornatural language understanding.

FIG. 9 is a flow diagram of the recommendation processing of ATRS 4 inone embodiment of the present invention. For each eligible document D,count the number of times the keyword w.di-elect cons.keywords(C)appears 190. Calculate the relevance score of document D to cluster Cusing the equation:

${{relevance}( {D,C} )} = \frac{\sum\limits_{w{{keywords}{(c)}}}{{{count}( {w,D_{1}} )}{{count}( {w,D} )}}}{\lbrack {\sum\limits_{w{{keywords}{(c)}}}{{{count}( {w,D_{1}} )}{{count}( {w,D} )}^{2}}} \rbrack^{{1/2}\;}}$

where w keywords(C) denotes one of the keywords of cluster C.

Rank eligible documents by relevance score and other applicationcriteria 194. Retain top scoring documents and apply other filtrationcriteria specific to this client 196. For example, the client may onlywant documents created within the last seven days. At the completion ofall clusters 198, the system proceeds to the balance of processing.

The presentation of recommendations may be through a set ordered byrelevance score, set ordered by popularity of document, a greeting to asite visitor, a notification of a sale, event, or promotion, a clientalert, for example, a sound indicating presence of a new document, or anew article obtained from a newswire as in “push” data feed deliverymethods, notification of TV shows and entertainment based on processingthe descriptions of previously viewed TV programs or purchased ticketsfor entertainment shows. Hard copy formats in the form of postcards,letters, or fliers may also be the medium of presentation.

Another embodiment of the present invention is conversion of therecommendation set of documents into files for faxing to the client,conversion to voice and presenting it as a voicemail, a pager or audioor video alert for the client. Advantageously, such recommendations canbe sent through a network and stored for later retrieval. In anotherembodiment, the system may serve a “community of interest” like a wineconnoisseur's Internet list or chat room where the recommendation mayconsist of the popular magazines or web pages viewed by experts of thecommunity of interest. Alternatively, the recommendation may bepresented to the client or requester as a set of Internet bookmarks.

There are several alternative embodiments of the present invention. In adocument classification application, customer e-mails sent to acompany's customer service representative (CSR) department can be routedto the CSR that had successfully resolved similar e-mails containing thesame issues. A similar application is the automatic classification ofpersonal e-mail wherein ATRS processes e-mails read and or responded toby the client, applying the clustering/keywordextraction/filtering/recommending steps to present the recommendede-mails to the client, treating the rest as miscellaneous. The clientmay further specify presentation of the top ten e-mails only, a veryuseful feature for e-mail access on wireless devices. Otherclassification applications are automatic routing of job postings to ajob category, and automatic classification of classified advertisementsor offers for sale or offers to swap items or services.

Other applications of ATRS involve research either in the Internet or inenterprise databases. For example, a client may be interested in“banking”. Instead of sifting through multitudes of documents thatcontains “banking”, the client may “mark” several documents and invokeATRS to present a set of recommended documents with a high measure ofstatistical relevance. This research may be invoked on a periodic basiswherein ATRS presents the recommended set of documents to the client inthe form of a notification or to clients in the “community of interest”application.

In another application of ATRS, online auction participants who havelost an auction are sent e-mail or other notification containing a listof auctions that are similar to the one they lost. This list isgenerated based on textual analysis of the description of the lostauction.

Another application of ATRS involves analyzing the text of news storiesor other content being viewed by a site visitor and displaying a list ofproducts whose descriptions contain similar themes or concepts. Forexample, a visitor to a web site featuring stories about pop stars mightread an article about Madonna and be presented a list of Madonna-relatedproducts such as musical recordings, clothing, etc. The presentation ofthe recommended products might be done immediately as the site visitoris browsing, or upon returning to the web site, or in an e-mail, orother delayed form of notification.

Similarly, ATRS can work in conjunction with a regular search engine tonarrow the results to a more precise recommended set of documents. Inone embodiment, ATRS 4 is a front-end system of a network search engine.ATRS 4 analyzes the text of an interest set of documents, groups theinterest set of documents into clusters; extracts keywords from the textof the documents grouped into the clusters; and communicates theselected keywords of the clusters to the search engine. The searchengine uses these keywords to search the network for documents thatmatches the keywords and other filtering criteria that may be set up forthe application.

FIG. 10A is an architectural diagrams where the requester device 2 maybe a PC used by a client to access a website and ATRS 4 is manually orautomatically invoked upon accessing the site. The document source 6 maybe at the website or may be the entire Internet. FIG. 10B shows analternative embodiment of the present invention wherein the requestordevice 2 is essentially unchanged but the application website 300 forATRS 4 only hosts the ATRS shell 300 or application proxy and the ATRSmodules 305 are operable in a remote site. Document source 6 may beoperable in a distributed manner at the same or different remote site asthe ATRS modules 305. Alternatively, document source 6 may be the entireInternet.

FIG. 11 is an architectural diagram illustrating the deployment ofmultiple applications of ATRS 4 in and outside the United States,according to the present invention. Requestor device 1310, is in theUnited States, and Requestor device 2312, is located outside of theUnited States. Requestor device 1310 and Requestor device 2312, arecoupled to ATRS 1314 in the United States and or ATRS 2316 locatedoutside of the United States. Document Source 1318 is in the UnitedStates whereas Document Source 2320 is outside the United States andboth are coupled to and provide eligible documents for ATRS 1314 and orATRS 2316.

FIG. 12 is an architectural diagram of an adaptive data recommendationsystem in an alternative embodiment of the present invention,illustrating the data requester device 330 invoking and receiving a setof recommended relevant data from an adaptive data recommendation system332 using data source 334.

FIG. 13 is an architectural diagram illustrating the major input andoutput of an adaptive data recommendation system in an alternativeembodiment of the present invention, illustrating the various types ofdata that are requested and returned to the requestor device. A documentinterest set 340, audio interest set 342, a video interest set 344, andor a multimedia interest set 346 are accessed by an adaptive datarecommendation system 332, utilizing a data source 334, a clientdatabase 348, and application parameters 358 to create a recommendeddata set comprising document recommended set 350, audio recommended set352, video recommended set 354, and multimedia recommended set 356. Asan example, based on the description of various artists and theirsinging styles, a requester device may specify certain singers with thetype of songs and lyrics desired, an adaptive data recommendation systemwould cluster the songs and artists, extract keywords of the lyrics orkey notes or note patterns in the artists' songs, and search sitescontaining libraries of artists and songs, and select for recommendationthe downloadable songs relevant to requestor's criteria. Therecommendation could be streaming audio or streaming video that can beplayed at the requester device.

One implementation of the present invention is on a Linux OS runningApache web server with a MySQL database. However, a person knowledgeablein the art will readily recognize that the present invention can beimplemented in different operating systems, different web servers withother types of data bases but not limited to Oracle and Informix.

A person knowledgeable in the art will readily recognize that thepresent invention can be implemented in a portable device comprising acontroller; memory; storage; input accessories such a keyboard,pressure-sensitive pad, or voice recognition equipment; a display forpresenting the recommended set; and communications equipment towirelessly-connect the portable device to an information network. In oneembodiment, the ATRS computer readable code can be loaded into theportable device by disk, tape, or a hardware plug-in, or downloaded froma site. In another embodiment, the logic and principles of the presentinvention can be designed and implemented in the circuitry of theportable device.

Foregoing described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. In particular, it is contemplatedthat functional implementation of the invention described herein may beimplemented equivalently in hardware, software, firmware, and/or otheravailable functional components or building blocks.

Other variations and embodiments are possible in light of aboveteachings, and it is thus intended that the scope of invention not belimited by this Detailed Description, but rather by claims following.

What is claimed is:
 1. A method for adaptive information recommendation,the method comprising: storing user-specific information in memory, theuser-specific information concerning interactions with a plurality ofdocuments; and executing instructions stored in memory, whereinexecution of the instructions by a processor: clusters an interest setof documents corresponding to the user-specific information concerninginteractions with the plurality of documents, constructs a recommendedset of documents for the cluster based on a relevance score of eachdocument in the cluster, wherein the relevance score is based onrelevance to keyword or natural language input and user-defined limit ondocument age, and provides the recommended set of documents for furtherinteraction.
 2. The method of claim 1, wherein the interactions includea previous view of a document by the user.
 3. The method of claim 1,wherein the interactions include searches previously conducted by theuser.
 4. The method of claim 1, wherein the interactions includeprevious e-mail messages sent by the user.
 5. The method of claim 1,further comprising ranking each document in the recommended set ofdocuments.
 6. The method of claim 1, wherein the recommended set ofdocuments is provided to the user by way of an e-mail message.
 7. Themethod of claim 1, wherein the recommended set of documents is providedto the user by way of an alert.
 8. The method of claim 1, wherein therelevance score is calculated using an algorithm based on a frequency ofappearance of the keyword or natural language input.
 9. A non-transitorycomputer-readable storage medium having embodied thereon a program, theprogram being executable by a processor to perform a method for adaptiveinformation recommendation, the method comprising: storing user-specificinformation in memory, the user-specific information concerninginteractions with a plurality of documents; clustering an interest setof documents corresponding to the user-specific information concerninginteractions with the plurality of documents; constructing a recommendedset of documents for the cluster based on a relevance score of eachdocument in the cluster, wherein the relevance score is based onrelevance to keyword or natural language input and user-defined limit ondocument age; and providing the recommended set of documents for furtherinteraction.
 10. The non-transitory computer-readable storage medium ofclaim 9, wherein the interactions include a previous view of a documentby the user.
 11. The non-transitory computer-readable storage medium ofclaim 9, wherein the interactions include searches previously conductedby the user.
 12. The non-transitory computer-readable storage medium ofclaim 9, wherein the interactions include previous e-mail messages sentby the user.
 13. The non-transitory computer-readable storage medium ofclaim 9, further comprising ranking each document in the recommended setof documents.
 14. The non-transitory computer-readable storage medium ofclaim 9, wherein the recommended set of documents is provided to theuser by way of an e-mail message.
 15. The non-transitorycomputer-readable storage medium of claim 9, wherein the recommended setof documents is provided to the user by way of an alert.
 16. Thenon-transitory computer-readable storage medium of claim 9, wherein therelevance score is calculated using an algorithm based on a frequency ofappearance of the keyword or natural language input.